Technical Field
Certain aspects of the present disclosure generally relate to machine learning and, more particularly, to improving systems and methods of detecting spatially diverse temporal patterns.
Background
Mobile devices, such as cell phones or personal digital assistants (PDAs), have several functions, each of which may be activated through the user selection of a unique sequence of keys or using on-screen menus. As mobile devices offer increased feature sets, accessing all of the features may become increasingly complex given a limited number of controls capable of being provided on a mobile device.
Recently, some mobile devices have been designed to include the ability to receive user input through recognition of user-controlled gestures. Some devices may receive user-controlled gestures by way of a touch-screen interface, while other devices may be configured to receive user-controlled gestures by acquiring images and implementing a computer-vision approach to tracking user input. One important aspect of gesture recognition is the ability to recognize a known pattern in the resultant trajectory data. However, the appearance of or the method in which the input gesture is drawn or motioned often varies from user to user, or even varies each time it is drawn by the same user. For example, slight variations may exist in how different users draw a particular character (e.g., number “2”). Recognizing a pattern in the trajectory data remains a significant challenge due to these variations.